Short Course on Fundamentals of Subspace-based Techniques with Applications in Signal and Image Processing | 18 – 19 May 2013




Course Overview

The course will cover the fundamentals of subspace-based techniques in linear algebra and statistical signal processing. It will outline the principle of orthogo-nality and demonstrates its relationship to the singular value decomposition
(SVD), the eigenvalue decomposition and the oriented energy. Moreover, the course will focus on the linear estimators, like the minimum Variance, the Time Domain Constraint and the Spectral Domain Constraint for signal and image denoising and will cover some of the widely used algorithms in the areas of array signal processing and spectrum estimation.
The course will also show the implementation of the different subspace tech-niques using MATLAB codes, in the following areas:
Signal and Image enhancement (Time Domain Constraint Estimator).

  •  Array Signal Processing (Multiple Signal Classification (MUSIC)).
  • Spectrum estimation (Modified covariance).
  • Pattern recognition (Data glove for signature verification).

Participant will get the opportunity to run MATLAB codes with the various applications and have the experience in writing similar codes.


Brochure Subspace Techniques

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